Beyond Collision Avoidance: Multi-Robot Yielding and Spatial Affordance in Emergency Evacuations

TL;DR

This study explores how multi-robot yielding strategies during emergency evacuations influence human psychological responses and safety perceptions, emphasizing the importance of environmental affordances and semantic understanding.

Contribution

It introduces a game-based virtual experiment to evaluate human responses to different robot yielding strategies, highlighting the significance of environmental cues and proactive space utilization.

Findings

Proactive yielding strategies like Hide are preferred over freezing and shortest path.

Utilizing environmental niches enhances psychological comfort and perceived safety.

Lack of niche utilization can cause expectation violations and perceived delays.

Abstract

As mobile service robots increasingly coexist with pedestrians, ensuring passively safe behaviour during confined emergency evacuations is critical. Existing multi-robot yielding strategies often focus solely on collision avoidance and macroscopic flow optimisation, overlooking environmental affordances and human spatial expectations. To bridge the gap between macroscopic theory and micro-level perception, we conducted a game-based virtual evacuation experiment (N=56). We investigated individual psychological responses to four multi-robot yielding strategies (Hide, LineEscape, Freeze, ShortestPath) across confined corridors with and without refuge niches. Our results establish a robust preference hierarchy (Hide > LineEscape > Freeze > ShortestPath), demonstrating that proactive space-yielding significantly outperforms freezing and efficiency-first approaches. Crucially, we found that environmental affordances heavily shape cognitive expectations. Actively utilising available niches amplifies the psychological comfort of proactive yielding (Hide). Conversely, failing to use an obvious niche (e.g., executing LineEscape) may trigger Expectation Violation. This is reflected in a drastically increased perceived cognitive delay, despite objectively unimpeded trajectories. Furthermore, prior robot interaction experience helps users decode complex social intents. Ultimately, this research demonstrates that safe human-robot interaction during emergencies must evolve from pure trajectory optimisation to semantically aware navigation. Future work will extend this framework to investigate complex interactions between robot swarms and pedestrian crowds.